Papers citing Machine Learning Directed Search for Ultraincompressible, Superhard Materials
Machine learning-guided design and development of multifunctional flexible Ag/poly (amic acid) composites using the differential evolution algorithm
Nanoscale · 2020-01-01
Mengyao Zhang, Jia Li, Ling Kang, Nan Zhang, Chun Huang, Yaqin He, Menghan Hu, Xiaofeng Zhou, Jian Zhang
Read original paper ↗ ·Machine-enabled inverse design of inorganic solid materials: promises and challenges
Chemical Science · 2020-01-01
Juhwan Noh, Geun Ho Gu, Sungwon Kim, Yousung Jung
Read original paper ↗ ·Machine-learning-assisted search for functional materials over extended chemical space
Materials Horizons · 2020-01-01
Vadim Korolev, Artem A. Mitrofanov, Artem A. Eliseev, Valery Tkachenko
Read original paper ↗ ·Molecular dynamics based descriptors for predicting supramolecular gelation
Chemical Science · 2020-01-01
Ruben Van Lommel, Jianyu Zhao, Wim M. De Borggraeve, Frank De Proft, Mercedes Alonso
Read original paper ↗ ·Quantum-mechanical transition-state model combined with machine learning provides catalyst design features for selective Cr olefin oligomerization
Chemical Science · 2020-01-01
Steven M. Maley, Doo‐Hyun Kwon, Nick Rollins, Johnathan C. Stanley, Orson L. Sydora, Steven M. Bischof, Daniel H. Ess
Read original paper ↗ ·Machine learning enabled high-throughput screening of hydrocarbon molecules for the design of next generation fuels
Fuel · 2020-01-07
Guozhu Li, Zheng Hu, Fang Hou, Xinyu Li, Li Wang, Xiangwen Zhang
Read original paper ↗ ·Crystal symmetry determination in electron diffraction using machine learning
Science · 2020-01-30
Kevin Kaufmann, Chaoyi Zhu, Alexander S. Rosengarten, Daniel Maryanovsky, Tyler Harrington, Eduardo Marin, Kenneth S. Vecchio
Read original paper ↗ ·Critical Temperature Prediction of Superconductors Based on Atomic Vectors and Deep Learning
Symmetry · 2020-02-08
Shaobo Li, Yabo Dan, Xiang Li, Tiantian Hu, Rongzhi Dong, Zhuo Cao, Jianjun Hu
Read original paper ↗ ·Atomic structures and orbital energies of 61,489 crystal-forming organic molecules
Scientific Data · 2020-02-18
Annika Stuke, Christian Künkel, Dorothea Golze, Milica Todorović, Johannes T. Margraf, Karsten Reuter, Patrick Rinke, Harald Oberhofer
Read original paper ↗ ·Compositionally-Restricted Attention-Based Network for Materials Property Prediction
ChemRxiv · 2020-02-20
Anthony Wang, Steven Kauwe, Ryan Murdock, Taylor D. Sparks
Read original paper ↗ ·Deformation behavior and amorphization in icosahedral boron-rich ceramics
Progress in Materials Science · 2020-03-20
Amnaya P. Awasthi, Ghatu Subhash
Read original paper ↗ ·Genetic algorithm-guided deep learning of grain boundary diagrams: Addressing the challenge of five degrees of freedom
Materials Today · 2020-04-04
Chongze Hu, Yunxing Zuo, Chi Chen, Shyue Ping Ong, Jian Shu Luo
Read original paper ↗ ·Machine Learning for Structural Materials
Annual Review of Materials Research · 2020-04-28
Taylor D. Sparks, Steven K. Kauwe, Marcus E. Parry, Aria Mansouri Tehrani, Jakoah Brgoch
Read original paper ↗ ·Machine Learning in Materials Discovery: Confirmed Predictions and Their Underlying Approaches
Annual Review of Materials Research · 2020-05-18
James E. Saal, Anton O. Oliynyk, Bryce Meredig
Read original paper ↗ ·Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices
Chemistry of Materials · 2020-05-19
Anthony Wang, Ryan Murdock, Steven K. Kauwe, Anton O. Oliynyk, Aleksander Gurlo, Jakoah Brgoch, Kristin Aslaug Persson, Taylor D. Sparks
Read original paper ↗ ·Machine-Learning-Enabled Exploration of Morphology Influence on Wire-Array Electrodes for Electrochemical Nitrogen Fixation
The Journal of Physical Chemistry Letters · 2020-05-27
Benjamin B. Hoar, Shengtao Lu, Chong Liu
Read original paper ↗ ·Material informatics for layered high-T C superconductors
APL Materials · 2020-06-01
Zhongli Liu, Peng Chao Kang, Yu Zhu, Lei Liu, Hong Yu Guo
Read original paper ↗ ·Targeting Productive Composition Space through Machine-Learning-Directed Inorganic Synthesis
Matter · 2020-06-05
Sogol Lotfi, Ziyan Zhang, Gayatri Viswanathan, Kaitlyn Fortenberry, Aria Mansouri Tehrani, Jakoah Brgoch
Read original paper ↗ ·Autofocused oracles for model-based design
arXiv (Cornell University) · 2020-06-14
Clara Fannjiang, Jennifer Listgarten
Read original paper ↗ ·General Protocol for the Accurate Prediction of Molecular 13C/1H NMR Chemical Shifts via Machine Learning Augmented DFT
Journal of Chemical Information and Modeling · 2020-06-30
Peng Gao, Jun Zhang, Jun Zhang, Qian Peng, Jie Zhang, Jie Zhang, Vassiliki‐Alexandra Glezakou
Read original paper ↗ ·Modeling the dielectric constants of crystals using machine learning
The Journal of Chemical Physics · 2020-07-10
Kazuki Morita, Daniel W. Davies, Keith T. Butler, Aron Walsh
Read original paper ↗ ·Interactive-quantum-chemical-descriptors enabling accurate prediction of an activation energy through machine learning
Polymer · 2020-07-13
K. Mikami
Read original paper ↗ ·A New Era of Inorganic Materials Discovery Powered by Data Science
Royal Society of Chemistry eBooks · 2020-07-15
Ya Zhuo, Aria Mansouri Tehrani, Jakoah Brgoch
Read original paper ↗ ·Accelerating inverse crystal structure prediction by machine learning: A case study of carbon allotropes
Frontiers of Physics · 2020-07-15
Wen Tong, Qun Wei, Haiyan Yan, Meiguang Zhang, Xuanmin Zhu
Read original paper ↗ ·Accelerated Modeling of Lithium Diffusion in Solid State Electrolytes using Artificial Neural Networks
Advanced Theory and Simulations · 2020-07-16
Karun Kumar Rao, Yan Li Yao, Lars Christian Grabow
Read original paper ↗ ·Extracting Knowledge from DFT: Experimental Band Gap Predictions Through Ensemble Learning
Integrating materials and manufacturing innovation · 2020-07-24
Steven K. Kauwe, Taylor Welker, Taylor D. Sparks
Read original paper ↗ ·Database of Two-Dimensional Hybrid Perovskite Materials: Open-Access Collection of Crystal Structures, Band Gaps, and Atomic Partial Charges Predicted by Machine Learning
Chemistry of Materials · 2020-08-11
Ekaterina I. Marchenko, Sergey A. Fateev, Andrey A. Petrov, Vadim Korolev, Artem A. Mitrofanov, Andrey V. Petrov, Eugene Alekseevich Goodilin, Alexey B. Tarasov
Read original paper ↗ ·Application of machine learning methods for predicting new superhard materials
Journal of Applied Physics · 2020-08-17
Efim A. Mazhnik, Артем Ромаевич Оганов
Read original paper ↗ ·Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm
npj Computational Materials · 2020-09-15
Alexander Dunn, Qi Wang, Alex M. Ganose, Daniel Dopp, Anubhav Jain
Read original paper ↗ ·Data-driven studies of magnetic two-dimensional materials
Scientific Reports · 2020-09-25
Trevor David Rhone, Wei Chen, Shaan A. Desai, Steven B. Torrisi, Daniel T. Larson, Amir Yacoby, Efthimios Kaxiras
Read original paper ↗ ·Element Code from Pseudopotential as Efficient Descriptors for a Machine Learning Model to Explore Potential Lead-Free Halide Perovskites
The Journal of Physical Chemistry Letters · 2020-10-06
Meng‐Huan Jao, Shun‐Hsiang Chan, Ming‐Chung Wu, Chao‐Sung Lai
Read original paper ↗ ·Machine learning in materials design: Algorithm and application*
Chinese Physics B · 2020-10-14
Zhilong Song, Xiwen Chen, Fanbin Meng, Guanjian Cheng, Chen Wang, Zhongti Sun, Wan‐Jian Yin
Read original paper ↗ ·MATCOR, a program for the cross-validation of material properties between databases
Computational Materials Science · 2020-10-20
Jorge Marquez Chavez, Boris Kiefer
Read original paper ↗ ·Machine learning approach for elucidating and predicting the role of synthesis parameters on the shape and size of TiO2 nanoparticles
Scientific Reports · 2020-11-03
Francesco Pellegrino, Raluca Isopescu, Letizia Pellutiè, Fabrizio Sordello, Andrea Mario Rossi, Erik Ortel, Gianmario Martra, Vasile‐Dan Hodoroaba, Valter Maurino
Read original paper ↗ ·Engineered two-dimensional nanomaterials: an emerging paradigm for water purification and monitoring
Materials Horizons · 2020-11-04
Minxiang Zeng, Mingfeng Chen, Dali Huang, Shijun Lei, Xuan Kai Zhang, Ling Wang, Zhengdong Cheng
Read original paper ↗ ·Toward Accurate Predictions of Atomic Properties via Quantum Mechanics Descriptors Augmented Graph Convolutional Neural Network: Application of This Novel Approach in NMR Chemical Shifts Predictions
The Journal of Physical Chemistry Letters · 2020-11-05
Peng Gao, Jie Zhang, Yuzhu Sun, Jianguo Yu
Read original paper ↗ ·Emerging materials intelligence ecosystems propelled by machine learning
Nature Reviews Materials · 2020-11-09
Rohit Batra, Le Song, Rampi Ramprasad
Read original paper ↗ ·The Role of Machine Learning in the Understanding and Design of Materials
Journal of the American Chemical Society · 2020-11-10
Seyed Mohamad Moosavi, Kevin Maik Jablonka, Berend Smit
Read original paper ↗ ·Learning Representations of Inorganic Materials from Generative Adversarial Networks
Symmetry · 2020-11-17
Tiantian Hu, Hui Song, Tao Jiang, Shaobo Li
Read original paper ↗ ·Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm
eScholarship (California Digital Library) · 2020-12-01
Dunn, A, Wang, Q, Ganose, A, Dopp, D, Jain, A
Read original paper ↗ ·Superhard Alloys: A Review of Empirical Modeling and Computational Strategies
IOP Conference Series Materials Science and Engineering · 2020-12-01
Chandramouli Ch., B. Venkatesh, M. Manzoor Hussain
Read original paper ↗ ·Finding the Next Superhard Material through Ensemble Learning
Advanced Materials · 2020-12-04
Ziyan Zhang, Aria Mansouri Tehrani, Anton O. Oliynyk, Blake Day, Jakoah Brgoch
Read original paper ↗ ·Predicting materials properties without crystal structure: deep representation learning from stoichiometry
Nature Communications · 2020-12-08
Rhys E. A. Goodall, Alpha A. Lee
Read original paper ↗ ·Discovery of Low-Modulus Ti-Nb-Zr Alloys Based on Machine Learning and First-Principles Calculations
ACS Applied Materials & Interfaces · 2020-12-09
Camilo Augusto Fernandes Salvador, Bruno Fedosse Zornio, Caetano Rodrigues Miranda
Read original paper ↗ ·Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders
Chemistry of Materials · 2020-12-14
Rohit Batra, Hanjun Dai, Tran Doan Huan, Lihua Chen, Chiho Kim, Will R. Gutekunst, Le Song, Rampi Ramprasad
Read original paper ↗ ·Artificial intelligence and machine learning in design of mechanical materials
Materials Horizons · 2020-12-17
Kai Guo, Zhenze Yang, Chi‐Hua Yu, Markus J. Buehler
Read original paper ↗ ·A general QSPR protocol for the prediction of atomic/inter-atomic properties: a fragment based graph convolutional neural network (F-GCN)
Physical Chemistry Chemical Physics · 2021-01-01
Peng Gao, Jie Zhang, Hongbo Qiu, Shuaifei Zhao
Read original paper ↗ ·A new material discovery platform of stable layered oxide cathodes for K-ion batteries
Energy & Environmental Science · 2021-01-01
Sohyun Park, Sunhyeon Park, Young Park, Muhammad Hilmy Alfaruqi, Jang‐Yeon Hwang, Jaekook Kim
Read original paper ↗ ·High-throughput computation of novel ternary B–C–N structures and carbon allotropes with electronic-level insights into superhard materials from machine learning
Journal of Materials Chemistry A · 2021-01-01
Mohammed Al‐Fahdi, Tao Ouyang, Ming Hu
Read original paper ↗ ·Machine learning analysis and prediction models of alkaline anion exchange membranes for fuel cells
Energy & Environmental Science · 2021-01-01
Xiuyang Zou, Ji Sheng Pan, Zhe Sun, Bowen Wang, Zhiyu Jin, Guodong Xu, Feng Yan
Read original paper ↗ ·